What Does a Customer Data Platform Do?

Customer Data Platforms (CDPs) are a key component of MarTech stacks. 

The CDP marketplace is evolving very rapidly and there are numerous CDP vendors across various industries. These include large enterprise software vendors such as Salesforce, Adobe, IBM and Microsoft as well as several niche CDP vendors such as Segment, Tealium, ActionIQ, Amperity, Lytics and Treasure Data.

These CDPs come in different varieties. While most CDP vendors claim that they can solve all your marketing needs, the fact remains that these CDPs are very different from each other. They might get ticks in terms of functionality but the way that functionality is offered varies drastically. Therefore, it is important to understand their strengths and weaknesses. Now, in order to do that, it is important to understand the Customer Data lifecycle, or how customer data moves as part of customer journey.

Key Stages in Customer Data Lifecycle

In this 6-minute video, I have explained the key CDP capabilities across different stages of customer data lifecycle.

 

Or if you prefer, you can continue reading it below. The five broad stages in customer data lifecycle are:

Figure: Customer Data Stages

Data Ingestion

This refers to a CDP’s capabilities for ingesting data from online and offline sources. A CDP brings in data from several different types of sources - zero-, first-, second- and third-party data sources. The key aspects to consider here include, support for different types of data sources and data integration capabilities

Data/Profile Management

Once the data is collected or ingested from different sources, a CDP applies different data management as well as data quality tasks including, de-duping data, cleansing and normalizing it. Once normalized data is available, the CDP then stitches a unified profile of users. The key capabilities here include are identity resolution, device and channel stitching, relationship graphs and so forth.

Segmentation

This is the stage where you actually start taking benefits of what is popularly known as a “Single View of Customers”. Essentially, once you have profiles or 360 degree views of all your users or customers, you can then segment users based on different attributes. You can slice and dice the profiles, create cohorts, group similar data and create audiences. You can create these segments based on rules, or you can use more sophisticated, AI/ML-based techniques to create segments. 

Analytics

This segmented data then forms the basis for you to run analytics. Some tools provide dashboards and reporting related to audience analytics, customer engagement and so on. Some of them provide advanced capabilities, such as drilldown reporting, predictive analytics and integration with external analytics products for more complex and sophisticated types of analytics.

Activation

The final step in any data lifecycle is activation or putting all this data and segments to work. Several CDPs mostly rely on integration with other platforms and APIs for activation. They typically do this by sending audiences or segments to external applications such as personalization engines. Some CDPs do provide some capabilities for journey management and activations.